For geometric and motion tasks that require weaker semantics and high-resolution object localization capabilities, such as pose estimation on COCO [1]} and CrowdPose [2]}, depth estimation on NYUv2 [3]} and KITTI [4]}, and video object tracking on GOT10k [5]}, TrackingNet [6]}, and LaSOT [7]}, MIM models outperform supervised counterparts by large margins. Note that, without bells and whistles, Swin-L with MIM pre-training could achieve state-of-the-art performance on these benchmarks, e.g., \(80.5\)  AP on COCO \(val\) , \(78.9\)  AP on COCO \(test\) -\(dev\) , and \(78.0\)  AP on CrowdPose of pose estimation, \(0.287\)  RMSE on NYUv2 and \(1.966\)  RMSE on KITTI of depth estimation, and \(70.7\)  SUC on LaSOT of video object tracking.
